Overview

Dataset statistics

Number of variables19
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory148.6 KiB
Average record size in memory152.1 B

Variable types

Numeric19

Alerts

mass_1 is highly correlated with mass_ratio and 1 other fieldsHigh correlation
mass_ratio is highly correlated with mass_1 and 1 other fieldsHigh correlation
a_1 is highly correlated with chi_p and 1 other fieldsHigh correlation
a_2 is highly correlated with spin2xHigh correlation
cos_tilt_1 is highly correlated with tilt_1 and 2 other fieldsHigh correlation
cos_tilt_2 is highly correlated with tilt_2 and 2 other fieldsHigh correlation
mass_2 is highly correlated with mass_1 and 1 other fieldsHigh correlation
tilt_1 is highly correlated with cos_tilt_1 and 2 other fieldsHigh correlation
tilt_2 is highly correlated with cos_tilt_2 and 2 other fieldsHigh correlation
chi_eff is highly correlated with cos_tilt_1 and 5 other fieldsHigh correlation
chi_p is highly correlated with a_1 and 2 other fieldsHigh correlation
spin1z is highly correlated with cos_tilt_1 and 2 other fieldsHigh correlation
spin2z is highly correlated with cos_tilt_2 and 2 other fieldsHigh correlation
spin1x is highly correlated with a_1 and 1 other fieldsHigh correlation
spin2x is highly correlated with a_2 and 1 other fieldsHigh correlation
mass_1 is highly correlated with mass_ratio and 1 other fieldsHigh correlation
mass_ratio is highly correlated with mass_1High correlation
a_1 is highly correlated with chi_p and 1 other fieldsHigh correlation
a_2 is highly correlated with spin2xHigh correlation
cos_tilt_1 is highly correlated with tilt_1 and 2 other fieldsHigh correlation
cos_tilt_2 is highly correlated with tilt_2 and 2 other fieldsHigh correlation
mass_2 is highly correlated with mass_1High correlation
tilt_1 is highly correlated with cos_tilt_1 and 2 other fieldsHigh correlation
tilt_2 is highly correlated with cos_tilt_2 and 2 other fieldsHigh correlation
chi_eff is highly correlated with cos_tilt_1 and 5 other fieldsHigh correlation
chi_p is highly correlated with a_1 and 2 other fieldsHigh correlation
spin1z is highly correlated with cos_tilt_1 and 2 other fieldsHigh correlation
spin2z is highly correlated with cos_tilt_2 and 2 other fieldsHigh correlation
spin1x is highly correlated with a_1 and 1 other fieldsHigh correlation
spin2x is highly correlated with a_2 and 1 other fieldsHigh correlation
mass_1 is highly correlated with mass_ratio and 1 other fieldsHigh correlation
mass_ratio is highly correlated with mass_1High correlation
a_1 is highly correlated with spin1xHigh correlation
a_2 is highly correlated with spin2xHigh correlation
cos_tilt_1 is highly correlated with tilt_1 and 1 other fieldsHigh correlation
cos_tilt_2 is highly correlated with tilt_2 and 1 other fieldsHigh correlation
mass_2 is highly correlated with mass_1High correlation
tilt_1 is highly correlated with cos_tilt_1 and 1 other fieldsHigh correlation
tilt_2 is highly correlated with cos_tilt_2 and 1 other fieldsHigh correlation
chi_eff is highly correlated with spin1zHigh correlation
chi_p is highly correlated with spin1xHigh correlation
spin1z is highly correlated with cos_tilt_1 and 2 other fieldsHigh correlation
spin2z is highly correlated with cos_tilt_2 and 1 other fieldsHigh correlation
spin1x is highly correlated with a_1 and 1 other fieldsHigh correlation
spin2x is highly correlated with a_2High correlation
mass_1 is highly correlated with mass_2High correlation
a_1 is highly correlated with chi_p and 2 other fieldsHigh correlation
a_2 is highly correlated with chi_p and 2 other fieldsHigh correlation
cos_tilt_1 is highly correlated with tilt_1 and 3 other fieldsHigh correlation
cos_tilt_2 is highly correlated with tilt_2 and 3 other fieldsHigh correlation
mass_2 is highly correlated with mass_1High correlation
tilt_1 is highly correlated with cos_tilt_1 and 3 other fieldsHigh correlation
tilt_2 is highly correlated with cos_tilt_2 and 3 other fieldsHigh correlation
sin_tilt_1 is highly correlated with cos_tilt_1 and 2 other fieldsHigh correlation
sin_tilt_2 is highly correlated with cos_tilt_2 and 2 other fieldsHigh correlation
chi_eff is highly correlated with cos_tilt_1 and 5 other fieldsHigh correlation
chi_p is highly correlated with a_1 and 3 other fieldsHigh correlation
spin1z is highly correlated with a_1 and 4 other fieldsHigh correlation
spin2z is highly correlated with a_2 and 4 other fieldsHigh correlation
spin1x is highly correlated with a_1 and 1 other fieldsHigh correlation
spin2x is highly correlated with a_2 and 1 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
mass_1 has unique values Unique
mass_ratio has unique values Unique
a_1 has unique values Unique
a_2 has unique values Unique
cos_tilt_1 has unique values Unique
cos_tilt_2 has unique values Unique
redshift has unique values Unique
mass_2 has unique values Unique
tilt_1 has unique values Unique
tilt_2 has unique values Unique
sin_tilt_1 has unique values Unique
sin_tilt_2 has unique values Unique
chi_eff has unique values Unique
chi_p has unique values Unique
spin1z has unique values Unique
spin2z has unique values Unique
spin1x has unique values Unique
spin2x has unique values Unique

Reproduction

Analysis started2022-07-20 16:03:49.325630
Analysis finished2022-07-20 16:04:41.710008
Duration52.38 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.5
Minimum0
Maximum999
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:41.798714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.95
Q1249.75
median499.5
Q3749.25
95-th percentile949.05
Maximum999
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.8194361
Coefficient of variation (CV)0.5782170893
Kurtosis-1.2
Mean499.5
Median Absolute Deviation (MAD)250
Skewness0
Sum499500
Variance83416.66667
MonotonicityStrictly increasing
2022-07-20T17:04:41.988332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
6711
 
0.1%
6581
 
0.1%
6591
 
0.1%
6601
 
0.1%
6611
 
0.1%
6621
 
0.1%
6631
 
0.1%
6641
 
0.1%
6651
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
9991
0.1%
9981
0.1%
9971
0.1%
9961
0.1%
9951
0.1%
9941
0.1%
9931
0.1%
9921
0.1%
9911
0.1%
9901
0.1%

mass_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.626600792
Minimum5.623636621
Maximum67.51876593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:42.161032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.623636621
5-th percentile5.729168808
Q16.29173147
median7.253185816
Q39.553942783
95-th percentile30.10042243
Maximum67.51876593
Range61.89512931
Interquartile range (IQR)3.262211313

Descriptive statistics

Standard deviation6.859717137
Coefficient of variation (CV)0.712579371
Kurtosis14.00250337
Mean9.626600792
Median Absolute Deviation (MAD)1.215118152
Skewness3.38353283
Sum9626.600792
Variance47.0557192
MonotonicityNot monotonic
2022-07-20T17:04:42.295735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.997086641
 
0.1%
7.7455817881
 
0.1%
8.0932996831
 
0.1%
8.4280731561
 
0.1%
6.297073471
 
0.1%
6.4199701811
 
0.1%
7.0182342831
 
0.1%
7.2326966981
 
0.1%
7.929358381
 
0.1%
5.9349085611
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
5.6236366211
0.1%
5.6268219241
0.1%
5.6274138431
0.1%
5.6284802841
0.1%
5.6321620951
0.1%
5.632210711
0.1%
5.6327753961
0.1%
5.6374196831
0.1%
5.6396370341
0.1%
5.642035791
0.1%
ValueCountFrequency (%)
67.518765931
0.1%
63.242104991
0.1%
41.380737621
0.1%
38.943944451
0.1%
38.543851981
0.1%
36.533965281
0.1%
36.51227741
0.1%
35.862298031
0.1%
35.685982281
0.1%
34.801208581
0.1%

mass_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9173670479
Minimum0.2164184813
Maximum0.999998618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:42.445823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2164184813
5-th percentile0.698486488
Q10.8828023248
median0.9595549119
Q30.9910985361
95-th percentile0.9995294399
Maximum0.999998618
Range0.7835801367
Interquartile range (IQR)0.1082962114

Descriptive statistics

Standard deviation0.1076333313
Coefficient of variation (CV)0.1173285345
Kurtosis6.051166137
Mean0.9173670479
Median Absolute Deviation (MAD)0.03656198448
Skewness-2.186967297
Sum917.3670479
Variance0.01158493402
MonotonicityNot monotonic
2022-07-20T17:04:42.838282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67574418611
 
0.1%
0.911630061
 
0.1%
0.98175406781
 
0.1%
0.88721596961
 
0.1%
0.99755579761
 
0.1%
0.9812928261
 
0.1%
0.96107358241
 
0.1%
0.96808005561
 
0.1%
0.91231022771
 
0.1%
0.99541280071
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.21641848131
0.1%
0.26163495871
0.1%
0.40135763111
0.1%
0.45166773711
0.1%
0.45847082691
0.1%
0.46822830991
0.1%
0.48353630111
0.1%
0.49061613041
0.1%
0.50419353611
0.1%
0.51332516861
0.1%
ValueCountFrequency (%)
0.9999986181
0.1%
0.99999807911
0.1%
0.99999180281
0.1%
0.99999087121
0.1%
0.99997896681
0.1%
0.99997780191
0.1%
0.9999753321
0.1%
0.99997168791
0.1%
0.99996051261
0.1%
0.99995845131
0.1%

a_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3033097225
Minimum0.002814354769
Maximum0.9067263586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:43.024012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.002814354769
5-th percentile0.06965293627
Q10.1678995537
median0.2775142124
Q30.4139646649
95-th percentile0.6149998706
Maximum0.9067263586
Range0.9039120038
Interquartile range (IQR)0.2460651112

Descriptive statistics

Standard deviation0.1723731244
Coefficient of variation (CV)0.5683072834
Kurtosis-0.1428599452
Mean0.3033097225
Median Absolute Deviation (MAD)0.1198093443
Skewness0.6118805586
Sum303.3097225
Variance0.02971249403
MonotonicityNot monotonic
2022-07-20T17:04:43.163769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72643007181
 
0.1%
0.24572742791
 
0.1%
0.69511006391
 
0.1%
0.30014905211
 
0.1%
0.040307557731
 
0.1%
0.31002424831
 
0.1%
0.45374339921
 
0.1%
0.2681498541
 
0.1%
0.10568875051
 
0.1%
0.44056859931
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.0028143547691
0.1%
0.0069009299421
0.1%
0.013400754591
0.1%
0.013626346381
0.1%
0.014021402111
0.1%
0.019129234731
0.1%
0.022323091671
0.1%
0.023620495681
0.1%
0.027541279771
0.1%
0.028158613231
0.1%
ValueCountFrequency (%)
0.90672635861
0.1%
0.87628465771
0.1%
0.83423127871
0.1%
0.83410210931
0.1%
0.83338661611
0.1%
0.77245089431
0.1%
0.77067634111
0.1%
0.77056856011
0.1%
0.76697590451
0.1%
0.7495173191
0.1%

a_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.308051265
Minimum0.006822058823
Maximum0.8534341915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:43.341691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.006822058823
5-th percentile0.06178657828
Q10.1834223214
median0.2903913276
Q30.4190143912
95-th percentile0.5981566641
Maximum0.8534341915
Range0.8466121326
Interquartile range (IQR)0.2355920698

Descriptive statistics

Standard deviation0.1666607679
Coefficient of variation (CV)0.5410163397
Kurtosis-0.3250305074
Mean0.308051265
Median Absolute Deviation (MAD)0.1190679866
Skewness0.4300229047
Sum308.051265
Variance0.02777581155
MonotonicityNot monotonic
2022-07-20T17:04:43.495767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07288763931
 
0.1%
0.46000287391
 
0.1%
0.5233186161
 
0.1%
0.45064844271
 
0.1%
0.42465977531
 
0.1%
0.51814330911
 
0.1%
0.30495776221
 
0.1%
0.25047709911
 
0.1%
0.41015237271
 
0.1%
0.27072505851
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.0068220588231
0.1%
0.0074975766871
0.1%
0.01231817061
0.1%
0.014819517131
0.1%
0.015701243421
0.1%
0.017606623931
0.1%
0.018793113191
0.1%
0.019765813051
0.1%
0.023265428481
0.1%
0.025440140091
0.1%
ValueCountFrequency (%)
0.85343419151
0.1%
0.82553123531
0.1%
0.81564730781
0.1%
0.78142421311
0.1%
0.77399101211
0.1%
0.76526422511
0.1%
0.75780075811
0.1%
0.74242163941
0.1%
0.738304341
0.1%
0.72833709711
0.1%

cos_tilt_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0401708181
Minimum-0.9984913569
Maximum0.9960745494
Zeros0
Zeros (%)0.0%
Negative476
Negative (%)47.6%
Memory size7.9 KiB
2022-07-20T17:04:43.661947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9984913569
5-th percentile-0.9005873094
Q1-0.4416828391
median0.05096471614
Q30.5415682331
95-th percentile0.9061318804
Maximum0.9960745494
Range1.994565906
Interquartile range (IQR)0.9832510722

Descriptive statistics

Standard deviation0.5763109244
Coefficient of variation (CV)14.34650703
Kurtosis-1.165360266
Mean0.0401708181
Median Absolute Deviation (MAD)0.4919845525
Skewness-0.07806218181
Sum40.1708181
Variance0.3321342815
MonotonicityNot monotonic
2022-07-20T17:04:43.824905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27444992731
 
0.1%
0.075351411571
 
0.1%
0.72465704441
 
0.1%
0.57273931821
 
0.1%
0.6157467471
 
0.1%
0.14043840241
 
0.1%
0.93890970761
 
0.1%
-0.88142130931
 
0.1%
0.87596650771
 
0.1%
0.44307697541
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-0.99849135691
0.1%
-0.99663586641
0.1%
-0.99503754951
0.1%
-0.99502758211
0.1%
-0.9945220371
0.1%
-0.99385010211
0.1%
-0.9918320411
0.1%
-0.99167400181
0.1%
-0.9892100981
0.1%
-0.97921343321
0.1%
ValueCountFrequency (%)
0.99607454941
0.1%
0.99376635431
0.1%
0.99179088231
0.1%
0.99026025641
0.1%
0.98782642211
0.1%
0.98759944681
0.1%
0.98671432571
0.1%
0.98469009661
0.1%
0.98343623761
0.1%
0.97915656411
0.1%

cos_tilt_2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02509283078
Minimum-0.9980090559
Maximum0.9987190093
Zeros0
Zeros (%)0.0%
Negative482
Negative (%)48.2%
Memory size7.9 KiB
2022-07-20T17:04:44.009581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9980090559
5-th percentile-0.8725400892
Q1-0.4560912546
median0.02447791071
Q30.5109141886
95-th percentile0.8804418133
Maximum0.9987190093
Range1.996728065
Interquartile range (IQR)0.9670054431

Descriptive statistics

Standard deviation0.5666975986
Coefficient of variation (CV)22.58404416
Kurtosis-1.168733717
Mean0.02509283078
Median Absolute Deviation (MAD)0.4831218489
Skewness-0.04703800471
Sum25.09283078
Variance0.3211461683
MonotonicityNot monotonic
2022-07-20T17:04:44.166397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1537397451
 
0.1%
-0.062906302341
 
0.1%
-0.17394053021
 
0.1%
-0.2107481391
 
0.1%
0.81060775451
 
0.1%
0.52609190441
 
0.1%
-0.05214844931
 
0.1%
-0.00064808667041
 
0.1%
0.80789369231
 
0.1%
-0.94151558221
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-0.99800905591
0.1%
-0.99703158851
0.1%
-0.99645796091
0.1%
-0.99563814341
0.1%
-0.99437981311
0.1%
-0.99098058021
0.1%
-0.98360964631
0.1%
-0.98205015351
0.1%
-0.97871361311
0.1%
-0.9771477541
0.1%
ValueCountFrequency (%)
0.99871900931
0.1%
0.99698109321
0.1%
0.9956931071
0.1%
0.99498618071
0.1%
0.99443795941
0.1%
0.99285837421
0.1%
0.99060533271
0.1%
0.990262731
0.1%
0.98426660761
0.1%
0.98083377041
0.1%

redshift
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.752046948
Minimum0.1938313478
Maximum2.298885814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:44.323756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1938313478
5-th percentile0.9028262783
Q11.495957144
median1.861470104
Q32.099366034
95-th percentile2.26113596
Maximum2.298885814
Range2.105054466
Interquartile range (IQR)0.6034088898

Descriptive statistics

Standard deviation0.4350073932
Coefficient of variation (CV)0.2482852378
Kurtosis0.06388492084
Mean1.752046948
Median Absolute Deviation (MAD)0.285395149
Skewness-0.8923273633
Sum1752.046948
Variance0.1892314321
MonotonicityNot monotonic
2022-07-20T17:04:44.452263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2141044221
 
0.1%
2.0467884131
 
0.1%
0.90311430221
 
0.1%
1.9627077111
 
0.1%
1.9366904261
 
0.1%
0.87844865251
 
0.1%
2.0868833391
 
0.1%
2.2815350111
 
0.1%
1.938036461
 
0.1%
1.6396865021
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.19383134781
0.1%
0.37036370571
0.1%
0.40379818181
0.1%
0.43551840771
0.1%
0.46972897051
0.1%
0.50807459851
0.1%
0.53570013181
0.1%
0.54707553351
0.1%
0.56551851761
0.1%
0.57946515951
0.1%
ValueCountFrequency (%)
2.2988858141
0.1%
2.2976829921
0.1%
2.2975734081
0.1%
2.2974036691
0.1%
2.2973181151
0.1%
2.2960921211
0.1%
2.2954800161
0.1%
2.2945864421
0.1%
2.2924693711
0.1%
2.2906287331
0.1%

mass_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.421065324
Minimum5.623477404
Maximum46.78488562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:44.603657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.623477404
5-th percentile5.72013851
Q16.150266266
median6.706992778
Q37.952771379
95-th percentile20.2162012
Maximum46.78488562
Range41.16140821
Interquartile range (IQR)1.802505113

Descriptive statistics

Standard deviation5.051466151
Coefficient of variation (CV)0.5998607013
Kurtosis13.78334621
Mean8.421065324
Median Absolute Deviation (MAD)0.7208016509
Skewness3.539863289
Sum8421.065324
Variance25.51731028
MonotonicityNot monotonic
2022-07-20T17:04:44.770313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1069615511
 
0.1%
7.0611051911
 
0.1%
7.9456298851
 
0.1%
7.4775210971
 
0.1%
6.2816821481
 
0.1%
6.2998706821
 
0.1%
6.7450395651
 
0.1%
7.0018294211
 
0.1%
7.234034751
 
0.1%
5.9076839531
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
5.6234774041
0.1%
5.6265997351
0.1%
5.6274060661
0.1%
5.6278427921
0.1%
5.631909851
0.1%
5.6321645421
0.1%
5.6327645751
0.1%
5.6356340231
0.1%
5.6390373281
0.1%
5.6401748431
0.1%
ValueCountFrequency (%)
46.784885621
0.1%
39.410192961
0.1%
38.862606361
0.1%
36.984366621
0.1%
31.673156191
0.1%
31.646703461
0.1%
30.874737261
0.1%
30.794307281
0.1%
30.617919521
0.1%
30.460366751
0.1%

tilt_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.524303248
Minimum0.08863432183
Maximum3.086655916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:45.113662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.08863432183
5-th percentile0.4367470117
Q10.9984948396
median1.519809519
Q32.028269911
95-th percentile2.69192206
Maximum3.086655916
Range2.998021594
Interquartile range (IQR)1.029775072

Descriptive statistics

Standard deviation0.6841124084
Coefficient of variation (CV)0.4488033528
Kurtosis-0.7752321142
Mean1.524303248
Median Absolute Deviation (MAD)0.5115657543
Skewness0.08069499681
Sum1524.303248
Variance0.4680097873
MonotonicityNot monotonic
2022-07-20T17:04:45.249283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2927787091
 
0.1%
1.4953734271
 
0.1%
0.76025974211
 
0.1%
0.9609526561
 
0.1%
0.90746299521
 
0.1%
1.4298921351
 
0.1%
0.35134778411
 
0.1%
2.6496592691
 
0.1%
0.50336048441
 
0.1%
1.1117682811
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.088634321831
0.1%
0.111715111
0.1%
0.12822148021
0.1%
0.1396824631
0.1%
0.15619447931
0.1%
0.15764686661
0.1%
0.16318821761
0.1%
0.17520918451
0.1%
0.18226185471
0.1%
0.2045299451
0.1%
ValueCountFrequency (%)
3.0866559161
0.1%
3.0595436251
0.1%
3.0419276121
0.1%
3.0418274881
0.1%
3.0368742531
0.1%
3.0306312931
0.1%
3.0136934541
0.1%
3.0124603371
0.1%
2.9945595851
0.1%
2.9373428931
0.1%

tilt_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.540238124
Minimum0.05062142411
Maximum3.078479975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:45.398618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05062142411
5-th percentile0.4940022221
Q11.034548407
median1.546315957
Q32.04439476
95-th percentile2.631174424
Maximum3.078479975
Range3.027858551
Interquartile range (IQR)1.009846353

Descriptive statistics

Standard deviation0.6682112333
Coefficient of variation (CV)0.433836316
Kurtosis-0.753760795
Mean1.540238124
Median Absolute Deviation (MAD)0.5033511446
Skewness0.01801718819
Sum1540.238124
Variance0.4465062523
MonotonicityNot monotonic
2022-07-20T17:04:45.528573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4164444181
 
0.1%
1.6337441921
 
0.1%
1.7456261221
 
0.1%
1.7831365511
 
0.1%
0.62560710511
 
0.1%
1.0167977751
 
0.1%
1.6229684411
 
0.1%
1.5714444141
 
0.1%
0.63022710071
 
0.1%
2.797896421
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.050621424111
0.1%
0.07772293061
0.1%
0.092843827561
0.1%
0.10017998471
0.1%
0.10551970741
0.1%
0.11958379891
0.1%
0.13718173481
0.1%
0.13966469541
0.1%
0.17762219991
0.1%
0.19610083381
0.1%
ValueCountFrequency (%)
3.0784799751
0.1%
3.0645227991
0.1%
3.0574008231
0.1%
3.048157851
0.1%
3.0355223071
0.1%
3.0071827521
0.1%
2.960290011
0.1%
2.9518359441
0.1%
2.9348931761
0.1%
2.9273973321
0.1%

sin_tilt_1
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7845622927
Minimum0.05490910826
Maximum0.999999976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:45.676723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05490910826
5-th percentile0.3115666902
Q10.6554671001
median0.8700044375
Q30.968934859
95-th percentile0.9987384509
Maximum0.999999976
Range0.9450908678
Interquartile range (IQR)0.3134677589

Descriptive statistics

Standard deviation0.2260470401
Coefficient of variation (CV)0.2881186646
Kurtosis0.3114611319
Mean0.7845622927
Median Absolute Deviation (MAD)0.1178914262
Skewness-1.108775238
Sum784.5622927
Variance0.05109726432
MonotonicityNot monotonic
2022-07-20T17:04:45.822510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96160139211
 
0.1%
0.99715704121
 
0.1%
0.68910969231
 
0.1%
0.81973756371
 
0.1%
0.78794412471
 
0.1%
0.99008941771
 
0.1%
0.34416356721
 
0.1%
0.47233089631
 
0.1%
0.48237192851
 
0.1%
0.89648357151
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.054909108261
0.1%
0.081957000331
0.1%
0.08851831491
0.1%
0.099500125571
0.1%
0.099599753131
0.1%
0.10452711611
0.1%
0.11073380031
0.1%
0.11148288261
0.1%
0.12755078421
0.1%
0.12787042611
0.1%
ValueCountFrequency (%)
0.9999999761
0.1%
0.99999961261
0.1%
0.99999896681
0.1%
0.99999843771
0.1%
0.99999406871
0.1%
0.9999938671
0.1%
0.9999931581
0.1%
0.99998863561
0.1%
0.99998674771
0.1%
0.99998397391
0.1%

sin_tilt_2
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7943732879
Minimum0.05059980708
Maximum0.99999979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:45.996069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05059980708
5-th percentile0.3278032316
Q10.6751660427
median0.8754102639
Q30.969716344
95-th percentile0.9987581495
Maximum0.99999979
Range0.9493999829
Interquartile range (IQR)0.2945503014

Descriptive statistics

Standard deviation0.2180916577
Coefficient of variation (CV)0.2745455582
Kurtosis0.860628556
Mean0.7943732879
Median Absolute Deviation (MAD)0.1120863307
Skewness-1.24386399
Sum794.3732879
Variance0.04756397117
MonotonicityNot monotonic
2022-07-20T17:04:46.157368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98811137571
 
0.1%
0.99801943721
 
0.1%
0.98475615861
 
0.1%
0.9775403941
 
0.1%
0.5855895051
 
0.1%
0.85042772071
 
0.1%
0.99863934391
 
0.1%
0.999999791
 
0.1%
0.58932824641
 
0.1%
0.33696944741
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.050599807081
0.1%
0.063070788271
0.1%
0.076993580971
0.1%
0.077644702091
0.1%
0.084092403081
0.1%
0.092710499771
0.1%
0.093298914181
0.1%
0.10001250061
0.1%
0.10532399981
0.1%
0.10587156021
0.1%
ValueCountFrequency (%)
0.999999791
0.1%
0.99999824991
0.1%
0.99999795041
0.1%
0.99998548751
0.1%
0.99998228661
0.1%
0.99998040211
0.1%
0.99997872241
0.1%
0.99997728451
0.1%
0.99997560221
0.1%
0.99997401461
0.1%

chi_eff
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009415239496
Minimum-0.5077789187
Maximum0.5926828031
Zeros0
Zeros (%)0.0%
Negative469
Negative (%)46.9%
Memory size7.9 KiB
2022-07-20T17:04:46.322760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.5077789187
5-th percentile-0.2264898542
Q1-0.08228420272
median0.01069765009
Q30.1046434715
95-th percentile0.2520672103
Maximum0.5926828031
Range1.100461722
Interquartile range (IQR)0.1869276742

Descriptive statistics

Standard deviation0.145057534
Coefficient of variation (CV)15.4066749
Kurtosis0.5192142538
Mean0.009415239496
Median Absolute Deviation (MAD)0.09371461847
Skewness-0.0138895577
Sum9.415239496
Variance0.02104168817
MonotonicityNot monotonic
2022-07-20T17:04:46.470707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1234919311
 
0.1%
-0.0041137683841
 
0.1%
0.20908292991
 
0.1%
0.046441593411
 
0.1%
0.18433046071
 
0.1%
0.15698381331
 
0.1%
0.20944653041
 
0.1%
-0.12017303061
 
0.1%
0.20649498941
 
0.1%
-0.029325670781
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-0.50777891871
0.1%
-0.44960836241
0.1%
-0.44231161561
0.1%
-0.42042089181
0.1%
-0.41740225241
0.1%
-0.41617170271
0.1%
-0.39458618161
0.1%
-0.37743847741
0.1%
-0.3707342241
0.1%
-0.3705877021
0.1%
ValueCountFrequency (%)
0.59268280311
0.1%
0.46989260491
0.1%
0.45952319641
0.1%
0.41430287231
0.1%
0.40818171531
0.1%
0.40350288611
0.1%
0.39573362031
0.1%
0.37677056441
0.1%
0.37385518311
0.1%
0.35993219721
0.1%

chi_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.313067429
Minimum0.01692527422
Maximum0.8254711028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:46.625293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01692527422
5-th percentile0.1105002489
Q10.2041276566
median0.2930475354
Q30.4065878377
95-th percentile0.5735424616
Maximum0.8254711028
Range0.8085458285
Interquartile range (IQR)0.2024601812

Descriptive statistics

Standard deviation0.1429601627
Coefficient of variation (CV)0.4566433601
Kurtosis-0.03339038136
Mean0.313067429
Median Absolute Deviation (MAD)0.09783204437
Skewness0.5873821192
Sum313.067429
Variance0.02043760813
MonotonicityNot monotonic
2022-07-20T17:04:46.764462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.69853616841
 
0.1%
0.41303037811
 
0.1%
0.50460919581
 
0.1%
0.38422553671
 
0.1%
0.2479817831
 
0.1%
0.43123533091
 
0.1%
0.29103283091
 
0.1%
0.24136078191
 
0.1%
0.21764816931
 
0.1%
0.39496251141
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.016925274221
0.1%
0.0247967341
0.1%
0.026926206051
0.1%
0.035085278851
0.1%
0.051902674291
0.1%
0.057313734251
0.1%
0.058088889051
0.1%
0.059455655981
0.1%
0.059507182221
0.1%
0.067114091171
0.1%
ValueCountFrequency (%)
0.82547110281
0.1%
0.82115226831
0.1%
0.75611387441
0.1%
0.74234073771
0.1%
0.73728487291
0.1%
0.73548070561
0.1%
0.72230560611
0.1%
0.70474426931
0.1%
0.69902319631
0.1%
0.69853616841
0.1%

spin1z
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01403194666
Minimum-0.6069444448
Maximum0.693310774
Zeros0
Zeros (%)0.0%
Negative476
Negative (%)47.6%
Memory size7.9 KiB
2022-07-20T17:04:46.911595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.6069444448
5-th percentile-0.3186853249
Q1-0.1033580074
median0.008624460475
Q30.1315826683
95-th percentile0.3534274367
Maximum0.693310774
Range1.300255219
Interquartile range (IQR)0.2349406758

Descriptive statistics

Standard deviation0.2018310029
Coefficient of variation (CV)14.38367803
Kurtosis0.6623634448
Mean0.01403194666
Median Absolute Deviation (MAD)0.1160913023
Skewness0.1267990421
Sum14.03194666
Variance0.04073575374
MonotonicityNot monotonic
2022-07-20T17:04:47.190094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19936868041
 
0.1%
0.018515908551
 
0.1%
0.50371640451
 
0.1%
0.17190716351
 
0.1%
0.024819247551
 
0.1%
0.043539310121
 
0.1%
0.42602408231
 
0.1%
-0.23635299541
 
0.1%
0.09257980571
 
0.1%
0.19520580241
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-0.60694444481
0.1%
-0.57427872651
0.1%
-0.55477374621
0.1%
-0.54760672741
0.1%
-0.54745044611
0.1%
-0.5438650631
0.1%
-0.53551440661
0.1%
-0.51683030971
0.1%
-0.51003025441
0.1%
-0.49806076581
0.1%
ValueCountFrequency (%)
0.6933107741
0.1%
0.63386225841
0.1%
0.62589198641
0.1%
0.62515653291
0.1%
0.60815849311
0.1%
0.60216694161
0.1%
0.57678976531
0.1%
0.57602501951
0.1%
0.5707458511
0.1%
0.55841557411
0.1%

spin2z
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004101692997
Minimum-0.6768312023
Maximum0.6985085789
Zeros0
Zeros (%)0.0%
Negative482
Negative (%)48.2%
Memory size7.9 KiB
2022-07-20T17:04:47.338964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.6768312023
5-th percentile-0.3454504464
Q1-0.1068070383
median0.005617724269
Q30.1230173411
95-th percentile0.3438602907
Maximum0.6985085789
Range1.375339781
Interquartile range (IQR)0.2298243794

Descriptive statistics

Standard deviation0.2012978176
Coefficient of variation (CV)49.07676359
Kurtosis0.6819059534
Mean0.004101692997
Median Absolute Deviation (MAD)0.115127549
Skewness-0.1257047224
Sum4.101692997
Variance0.04052081136
MonotonicityNot monotonic
2022-07-20T17:04:47.482556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011205727081
 
0.1%
-0.028937079861
 
0.1%
-0.091026317551
 
0.1%
-0.094973320621
 
0.1%
0.34423250691
 
0.1%
0.27259100031
 
0.1%
-0.01590307441
 
0.1%
-0.00016233086921
 
0.1%
0.33135951481
 
0.1%
-0.2548918611
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-0.67683120231
0.1%
-0.67384916161
0.1%
-0.67382804541
0.1%
-0.64052132421
0.1%
-0.62889698141
0.1%
-0.57262425661
0.1%
-0.56796326681
0.1%
-0.56738956611
0.1%
-0.54627825161
0.1%
-0.51462964111
0.1%
ValueCountFrequency (%)
0.69850857891
0.1%
0.58893717871
0.1%
0.581580771
0.1%
0.54521452581
0.1%
0.53930613361
0.1%
0.52924375571
0.1%
0.52306448061
0.1%
0.49272318611
0.1%
0.49195681141
0.1%
0.48993326311
0.1%

spin1x
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2375950746
Minimum0.00275723412
Maximum0.8211522683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:47.648312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.00275723412
5-th percentile0.04102676382
Q10.1150174239
median0.2039490533
Q30.3297625414
95-th percentile0.5372919756
Maximum0.8211522683
Range0.8183950341
Interquartile range (IQR)0.2147451175

Descriptive statistics

Standard deviation0.1560788164
Coefficient of variation (CV)0.6569109933
Kurtosis0.1754695911
Mean0.2375950746
Median Absolute Deviation (MAD)0.1024127162
Skewness0.8327184669
Sum237.5950746
Variance0.02436059694
MonotonicityNot monotonic
2022-07-20T17:04:47.780527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.69853616841
 
0.1%
0.24502883491
 
0.1%
0.47900708231
 
0.1%
0.24604345271
 
0.1%
0.031760103291
 
0.1%
0.30695172751
 
0.1%
0.15616194691
 
0.1%
0.12665546091
 
0.1%
0.050981286411
 
0.1%
0.39496251141
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.002757234121
0.1%
0.0037507502981
0.1%
0.0060750006331
0.1%
0.0080437001631
0.1%
0.0088634834181
0.1%
0.011933668871
0.1%
0.012483056691
0.1%
0.01248867171
0.1%
0.013042213131
0.1%
0.013253464161
0.1%
ValueCountFrequency (%)
0.82115226831
0.1%
0.74234073771
0.1%
0.73728487291
0.1%
0.73548070561
0.1%
0.72230560611
0.1%
0.70474426931
0.1%
0.69853616841
0.1%
0.67100368981
0.1%
0.66877583221
0.1%
0.65859696831
0.1%

spin2x
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2432424853
Minimum0.0009497061961
Maximum0.82551367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2022-07-20T17:04:47.921133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0009497061961
5-th percentile0.0381356765
Q10.1283752891
median0.2178275118
Q30.3435045775
95-th percentile0.5325674671
Maximum0.82551367
Range0.8245639638
Interquartile range (IQR)0.2151292884

Descriptive statistics

Standard deviation0.1516660778
Coefficient of variation (CV)0.6235180407
Kurtosis0.1534832768
Mean0.2432424853
Median Absolute Deviation (MAD)0.1036747339
Skewness0.7273775674
Sum243.2424853
Variance0.02300259916
MonotonicityNot monotonic
2022-07-20T17:04:48.065103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.072021105541
 
0.1%
0.45909180941
 
0.1%
0.515341231
 
0.1%
0.44052705621
 
0.1%
0.24867630761
 
0.1%
0.44064343331
 
0.1%
0.30454281961
 
0.1%
0.25047704651
 
0.1%
0.24171437861
 
0.1%
0.091226073361
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.00094970619611
0.1%
0.0038556403871
0.1%
0.010414448981
0.1%
0.011466890481
0.1%
0.011950711011
0.1%
0.012104584131
0.1%
0.012666457751
0.1%
0.012861514681
0.1%
0.013193138281
0.1%
0.013312089571
0.1%
ValueCountFrequency (%)
0.825513671
0.1%
0.77972960221
0.1%
0.74529523521
0.1%
0.71119839851
0.1%
0.6956664461
0.1%
0.68844397251
0.1%
0.68407655391
0.1%
0.67894729691
0.1%
0.67532082411
0.1%
0.67001292661
0.1%

Interactions

2022-07-20T17:04:38.630473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:52.203464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:54.766776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:57.517689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:00.235060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:02.712082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:05.254928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:08.247169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:11.217405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:13.421628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:15.839780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:18.613996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:21.075344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:23.659426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.142759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:28.488135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:31.026997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:33.669556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.251979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:38.761181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:52.345161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:54.900070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:57.680141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:00.353284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:02.854981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:05.403021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:08.401411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:11.329709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:13.527001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:15.983366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:18.730057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:21.182334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:23.796579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.260403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:28.618531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:31.255443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:33.798260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.360727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:38.897732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:52.476476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:55.039556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:57.817688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:00.479890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:03.000802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:05.551048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:08.559423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:11.442656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:13.636455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:16.150548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:18.848213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:21.291474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:23.941026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.379421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:28.753951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:31.380376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:33.950483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.474603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:39.051795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:52.619703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:55.190958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:57.974176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:00.635124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:03.143335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:05.712529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:08.730959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:11.581165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:13.758507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:16.322325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:18.985332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:21.410785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:24.078974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.517321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:28.898518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:31.527049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:34.253547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.607660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:39.177608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:52.734850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:55.327861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:58.106663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:00.757070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:03.261138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:05.854676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:08.868314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:11.692638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:13.980151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:16.460396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:19.118304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:21.523361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:24.188614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.632138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:29.024764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:31.658622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:34.377519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.715691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:39.294389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:53.041637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:55.465953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:58.231339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:00.880997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:03.379417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:05.988312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:09.011666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:11.803000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:14.087030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:16.607568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:19.246932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:21.636558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:24.301689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.750500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:29.156904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:31.785686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:34.511266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.823853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:39.432979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:53.173215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:55.632521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:58.373798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:01.013911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:03.523073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:06.154839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:09.186711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:11.933944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:14.213720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:16.767033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:19.383132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:21.770915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:24.445893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.886471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:29.310209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:31.934091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:34.671723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:37.063707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:39.576452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-20T17:04:23.404631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:25.884601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:28.134093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:30.784246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:33.421672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.027708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:38.401743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:40.977199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:54.639879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:03:57.383245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:00.104972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:02.586426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:05.125947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:08.093222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:10.903296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:13.318087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:15.714996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:18.502195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:20.967034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:23.525153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:26.015131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:28.242299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:30.907486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:33.544240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:36.139967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-20T17:04:38.509295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-20T17:04:48.212221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-20T17:04:48.538558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-20T17:04:48.830524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-20T17:04:49.157132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-20T17:04:41.159797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-20T17:04:41.572418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0mass_1mass_ratioa_1a_2cos_tilt_1cos_tilt_2redshiftmass_2tilt_1tilt_2sin_tilt_1sin_tilt_2chi_effchi_pspin1zspin2zspin1xspin2x
0011.9970870.6757440.7264300.0728880.2744500.1537401.2141048.1069621.2927791.4164440.9616010.9881110.1234920.6985360.1993690.0112060.6985360.072021
118.8614880.9220370.1885290.0857950.584701-0.6516200.5080758.1706190.9462852.2805150.8112490.7585450.0305330.1529440.110233-0.0559050.1529440.065079
227.9696380.9914700.2084360.2487100.6827500.4540731.7611687.9016610.8192771.0994640.7306520.8909640.1276840.2194330.1423090.1129320.1522940.221591
336.7675220.9343000.2973950.3332560.959535-0.6845691.4062226.3228970.2854512.3248080.2815900.7289480.0373330.2247740.285361-0.2281370.0837440.242927
445.7197750.9983940.2209780.1222300.240138-0.2700341.6495245.7105891.3282881.8442250.9707390.9628510.0100640.2145120.053065-0.0330060.2145120.117689
556.6735490.9881830.2832670.437678-0.029380-0.3967922.2783946.5946851.6001801.9788160.9995680.917908-0.0905030.396327-0.008322-0.1736670.2831450.401748
666.3139390.9695540.3406350.1977430.385053-0.6611161.3159956.1217041.1755312.2931010.9228950.7502840.0022400.3143700.131163-0.1307310.3143700.148363
776.6601610.9973860.2662930.197848-0.079090-0.3153211.7108086.6427501.6499691.8915910.9968680.948985-0.0416960.265459-0.021061-0.0623860.2654590.187755
886.7115320.9592230.6137280.1384020.859630-0.4995441.0760376.4378560.5362512.0938690.5109170.8662890.2354300.3135640.527579-0.0691380.3135640.119896
9910.8832310.9246080.1569820.4525870.5338810.8362342.26451610.0627251.0076120.5804180.8455590.5483730.2253680.2269210.0838100.3784680.1327380.248187

Last rows

Unnamed: 0mass_1mass_ratioa_1a_2cos_tilt_1cos_tilt_2redshiftmass_2tilt_1tilt_2sin_tilt_1sin_tilt_2chi_effchi_pspin1zspin2zspin1xspin2x
9909905.8389650.9986030.3974250.1506570.2828290.5567851.4549425.8308081.2840540.9802860.9591700.8306560.0981530.3811980.1124030.0838830.3811980.125144
9919917.0646570.9998040.1899900.271382-0.1066230.8629721.9203507.0632731.6776230.5296740.9942990.5052520.1069560.188907-0.0202570.2341950.1889070.137116
9929926.3240110.9622270.1054270.226353-0.925340-0.2973322.2296296.0851372.7527271.8726940.3791390.954774-0.0827200.206812-0.097556-0.0673020.0399720.216116
99399310.8832930.8477260.4137680.2908680.4872010.6961191.5374649.2260551.0619150.8008190.8732900.7179260.2019970.3613400.2015880.2024790.3613400.208822
9949946.1003190.9982380.3970640.4770170.6692840.3557430.8093286.0895730.8375511.2070880.7430060.9345840.2177640.4449150.2657490.1696950.2950210.445812
9959958.4730710.8266850.3511730.0379660.050601-0.1904452.1598167.0045601.5201741.7624120.9987190.9816980.0064560.3507230.017770-0.0072300.3507230.037271
99699613.5483340.8629520.4035940.272363-0.2887020.9987192.15011511.6915681.8636680.0506210.9574190.0506000.0634570.386408-0.1165180.2720140.3864080.013782
9979976.0119350.9982840.5368280.311054-0.514118-0.9481050.6087056.0016162.1107752.8180200.8577190.317956-0.2854440.460448-0.275993-0.2949120.4604480.098901
9989987.6756010.9676600.3922960.2534650.723111-0.3513981.8051477.4273710.7625001.9298600.6907320.9362260.1003660.2709710.283674-0.0890670.2709710.237301
99999912.8420000.8472090.7217070.1426320.6309270.3966412.06853610.8798610.8880491.1629420.7758420.9179740.2724510.5599310.4553440.0565740.5599310.130932